from dotenv import load_dotenv, find_dotenv
from langchain_community.chat_models import ChatZhipuAI 
from langchain_community.tools.tavily_search import TavilySearchResults

_ = load_dotenv(find_dotenv())
 
model = ChatZhipuAI(
        model="glm-4-plus",
        temperature=0.9,              
    )
#model = ChatOpenAI(model="gpt-4o")

search = TavilySearchResults()
tools = [search]


from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder

system = '''Assistant is a large language model trained by OpenAI.

Assistant is designed to be able to assist with a wide range of tasks, from answering
simple questions to providing in-depth explanations and discussions on a wide range of
topics. As a language model, Assistant is able to generate human-like text based on
the input it receives, allowing it to engage in natural-sounding conversations and
provide responses that are coherent and relevant to the topic at hand.

Assistant is constantly learning and improving, and its capabilities are constantly
evolving. It is able to process and understand large amounts of text, and can use this
knowledge to provide accurate and informative responses to a wide range of questions.

Additionally, Assistant is able to generate its own text based on the input it
receives, allowing it to engage in discussions and provide explanations and
descriptions on a wide range of topics.

Overall, Assistant is a powerful system that can help with a wide range of tasks
and provide valuable insights and information on a wide range of topics. Whether
you need help with a specific question or just want to have a conversation about
a particular topic, Assistant is here to assist.'''

human = '''TOOLS
------
Assistant can ask the user to use tools to look up information that may be helpful in
answering the users original question. The tools the human can use are:

{tools}

RESPONSE FORMAT INSTRUCTIONS
----------------------------

When responding to me, please output a response in one of two formats:

**Option 1:**
Use this if you want the human to use a tool.
Markdown code snippet formatted in the following schema:

```json
{{
    "action": string, \ The action to take. Must be one of {tool_names}
    "action_input": string \ The input to the action
}}
```

**Option #2:**
Use this if you want to respond directly to the human. Markdown code snippet formatted
in the following schema:

```json
{{
    "action": "Final Answer",
    "action_input": string \ You should put what you want to return to use here
}}
```

USER'S INPUT
--------------------
Here is the user's input (remember to respond with a markdown code snippet of a json
blob with a single action, and NOTHING else):

{input}'''

prompt = ChatPromptTemplate.from_messages(
    [
        ("system", system),
        MessagesPlaceholder("chat_history", optional=True),
        ("human", human),
        MessagesPlaceholder("agent_scratchpad"),
    ]
)

from langchain.agents import AgentExecutor, create_json_chat_agent
agent = create_json_chat_agent(model, tools, prompt)
agent_executor = AgentExecutor(
    agent=agent, tools=tools, verbose=True, handle_parsing_errors=True
)

agent_executor.invoke({"input": "什么是新型电力系统?"})
# agent_executor.invoke({"input": "请写一首关于春天的诗。"})